Why the Voynich Manuscript Might Not Be a Real Language

[Jorge_Stolfi]

Perplexity: This is a measure of how well a model predicts a sequence. Low perplexity means the model can easily predict the next character — the text is “regular” or “learnable.” High perplexity means the text is less predictable, like a noisy or complex system. It’s often used to evaluate how well a language model fits a dataset.

That should be a simple function of the next-character entropy, no?

But please, please, please people: the character entropy is a property of the encoding.  Not of the language, not of the text.  IIff yyoouu  jjuusstt  wwrriittee  eevveerryy  lleetttteerr  ttwwiiccee, the average per-character entropy will be halved.  iF yOU raNdOmLY cAPitAliZe eACh lEtTer, you will add 1 to the average per-character entropy. If you insert two random capital letters after each letter, lMTiGUkSHeWP tNNhLOiZQsDB, you will add at leasr 3 to the per-character entropy.

The low per-character entropy of Voynichese could mean simply that the "spelling system" that the Author invented for its language is inefficient.  Which is not surprising, since the theory needed to design good encodings was 600 years in the future.  Whereas the usual spelling systems for natural languages have become somewhat more efficient through centuries of use, which explains why they have higher per-character entropy than Voynichese. 

By the way, one possible explanation for the differences between the "languages" (actually just word distributions) A and B is that the Author made some simplifications to the spelling between the writing of the two parts.